import logging
from pathlib import Path
from typing import Dict, Any
import random
import numpy as np

logger = logging.getLogger(__name__)

class ModelInference:
    """Model inference class"""
    
    def __init__(self, config):
        self.config = config
        self.demo_mode = config.get('model.demo_mode', True)
        
        if self.demo_mode:
            logger.info("Running in demo mode - using mock predictions")
        else:
            self.load_model()
            
    def load_model(self):
        """Load model checkpoint"""
        try:
            checkpoint_dir = self.config.get('model.checkpoint_dir')
            if not checkpoint_dir:
                raise ValueError("Model checkpoint directory not specified")
                
            # TODO: Implement actual model loading
            raise NotImplementedError("Model loading not implemented")
            
        except Exception as e:
            logger.error(f"Failed to load model: {e}")
            raise
            
    def predict(self, image: np.ndarray) -> Dict[str, Any]:
        """Run inference on image"""
        try:
            if self.demo_mode:
                return self._mock_predict()
            
            # TODO: Implement actual prediction
            raise NotImplementedError("Model prediction not implemented")
            
        except Exception as e:
            logger.error(f"Prediction failed: {e}")
            raise
            
    def _mock_predict(self) -> Dict[str, Any]:
        """Generate mock prediction results for demo mode"""
        try:
            count = float(random.randint(50, 200))
            confidence = float(random.uniform(0.8, 0.99))
            
            return {
                'count': count,
                'confidence': confidence,
                'boxes': [],  # Placeholder for detection boxes
                'scores': [],  # Placeholder for detection scores
                'statistics': {
                    'count_stats': {
                        'mean': count,
                        'std': 0.0,
                        'cv': 0.0,
                        'min': count,
                        'max': count,
                        'median': count,
                        'q1': count,
                        'q3': count
                    },
                    'confidence_stats': {
                        'mean': confidence,
                        'std': 0.0,
                        'min': confidence,
                        'max': confidence
                    }
                }
            }
        except Exception as e:
            logger.error(f"Mock prediction failed: {e}")
            raise ValueError("Failed to generate mock prediction")
